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Key frame-based video representation is a procedure to summarize video content by mapping the entire video stream to several representative video frames. However, the existing methods are either computational expensive to extract the key frames at higher levels rather than shot level or ineffective to lay out the key frames sequentially. To overcome the shortcomings, we present a new hierarchical key frame tree-based video representation technique to model the video content hierarchically. Concretely, by projecting video frames from illumination-invariant raw feature space into low dimensional independent component analysis (ICA) subspace, each video frame is represented by a two-dimensional compact feature vector. A new kD-tree-based method is then employed to extract the key frames at shot level. A hierarchical agglomerative clustering-based method is applied to process the key frames hierarchically. Experimental results show that the proposed method is computationally efficient to model the semantic video content hierarchically. [ABSTRACT FROM AUTHOR]